import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)
def    snake_case		(__lowercase   ,     __lowercase		)			->			str:

						'''simple docstring'''



						_snake_case		:	Optional[Any]							=       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
						_snake_case		:	Dict							=       Image.open(requests.get(__snake_case   ,     stream=__snake_case		).raw		).convert("RGB"		)

						_snake_case		:	Dict							=       transforms.Compose(
						    [
						        transforms.Resize((image_size, image_size)   ,     interpolation=InterpolationMode.BICUBIC		),
						        transforms.ToTensor(),
						        transforms.Normalize((0.48145466, 0.4578275, 0.40821073)   ,     (0.26862954, 0.26130258, 0.27577711)		),
						    ]		)
						_snake_case		:	List[str]							=       transform(__snake_case		).unsqueeze(0		).to(__snake_case		)
						return image
def    snake_case		(__lowercase		)			->			Any:

						'''simple docstring'''



						if "visual_encoder" in key:
												_snake_case		:	Tuple							=       re.sub("visual_encoder*"   ,     "vision_model.encoder"   ,     __snake_case		)
						if "blocks" in key:
												_snake_case		:	Tuple							=       re.sub(r"blocks"   ,     "layers"   ,     __snake_case		)
						if "attn" in key:
												_snake_case		:	int							=       re.sub(r"attn"   ,     "self_attn"   ,     __snake_case		)
						if "norm1" in key:
												_snake_case		:	List[Any]							=       re.sub(r"norm1"   ,     "layer_norm1"   ,     __snake_case		)
						if "norm2" in key:
												_snake_case		:	Optional[int]							=       re.sub(r"norm2"   ,     "layer_norm2"   ,     __snake_case		)
						if "encoder.norm" in key:
												_snake_case		:	Any							=       re.sub(r"encoder.norm"   ,     "post_layernorm"   ,     __snake_case		)
						if "encoder.patch_embed.proj" in key:
												_snake_case		:	Dict							=       re.sub(r"encoder.patch_embed.proj"   ,     "embeddings.patch_embedding"   ,     __snake_case		)

						if "encoder.pos_embed" in key:
												_snake_case		:	List[Any]							=       re.sub(r"encoder.pos_embed"   ,     "embeddings.position_embedding"   ,     __snake_case		)
						if "encoder.cls_token" in key:
												_snake_case		:	List[str]							=       re.sub(r"encoder.cls_token"   ,     "embeddings.class_embedding"   ,     __snake_case		)

						if "self_attn" in key:
												_snake_case		:	List[Any]							=       re.sub(r"self_attn.proj"   ,     "self_attn.projection"   ,     __snake_case		)

						return key
@torch.no_grad()
def    snake_case		(__lowercase   ,     __lowercase=None		)			->			Optional[Any]:

						'''simple docstring'''



						if config_path is not None:
												_snake_case		:	str							=       BlipConfig.from_pretrained(__snake_case		)
						else:
												_snake_case		:	Any							=       BlipConfig(projection_dim=512   ,     text_config={}   ,     vision_config={}		)

						_snake_case		:	Union[str, Any]							=       BlipForConditionalGeneration(__snake_case		).eval()

						_snake_case		:	Optional[Any]							=       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

						_snake_case		:	Optional[int]							=       blip_decoder(pretrained=__snake_case   ,     image_size=384   ,     vit="base"		)
						_snake_case		:	str							=       pt_model.eval()

						_snake_case		:	Union[str, Any]							=       pt_model.state_dict()
						for key in modified_state_dict.copy():
												_snake_case		:	Union[str, Any]							=       modified_state_dict.pop(__snake_case		)
												_snake_case		:	Optional[Any]							=       rename_key(__snake_case		)
												_snake_case		:	int							=       value

						hf_model.load_state_dict(__snake_case		)

						_snake_case		:	List[str]							=       384
						_snake_case		:	int							=       load_demo_image(image_size=__snake_case   ,     device="cpu"		)
						_snake_case		:	str							=       BertTokenizer.from_pretrained("bert-base-uncased"		)
						_snake_case		:	Optional[Any]							=       tokenizer(["a picture of"]		).input_ids

						_snake_case		:	List[Any]							=       hf_model.generate(__snake_case   ,     __snake_case		)

						assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

						_snake_case		:	Tuple							=       hf_model.generate(__snake_case		)

						assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

						if pytorch_dump_folder_path is not None:
												hf_model.save_pretrained(__snake_case		)

						# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
						_snake_case		:	Optional[int]							=       (
						    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
						)

						_snake_case		:	Dict							=       blip_vqa(pretrained=__snake_case   ,     image_size=__snake_case   ,     vit="base"		)
						vqa_model.eval()

						_snake_case		:	Union[str, Any]							=       vqa_model.state_dict()
						for key in modified_state_dict.copy():
												_snake_case		:	Tuple							=       modified_state_dict.pop(__snake_case		)
												_snake_case		:	Dict							=       rename_key(__snake_case		)
												_snake_case		:	Dict							=       value

						_snake_case		:	List[Any]							=       BlipForQuestionAnswering(__snake_case		)

						hf_vqa_model.load_state_dict(__snake_case		)

						_snake_case		:	Dict							=       ["How many dogs are in this image?"]
						_snake_case		:	Any							=       tokenizer(__snake_case   ,     return_tensors="pt"		).input_ids

						_snake_case		:	Tuple							=       hf_vqa_model.generate(__snake_case   ,     __snake_case		)
						print(tokenizer.decode(answer[0]		)		)

						assert tokenizer.decode(answer[0]		) == "[UNK] 1 [SEP]"
						if pytorch_dump_folder_path is not None:
												hf_vqa_model.save_pretrained(pytorch_dump_folder_path + "_vqa"		)

						_snake_case		:	int							=       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

						_snake_case		:	int							=       blip_itm(pretrained=__snake_case   ,     image_size=__snake_case   ,     vit="base"		)
						itm_model.eval()

						_snake_case		:	Tuple							=       itm_model.state_dict()
						for key in modified_state_dict.copy():
												_snake_case		:	Dict							=       modified_state_dict.pop(__snake_case		)
												_snake_case		:	Any							=       rename_key(__snake_case		)
												_snake_case		:	str							=       value

						_snake_case		:	Tuple							=       BlipForImageTextRetrieval(__snake_case		)

						_snake_case		:	List[Any]							=       ["A picture of a woman with a dog sitting in a beach"]
						_snake_case		:	Optional[int]							=       tokenizer(
						    __snake_case   ,     return_tensors="pt"   ,     padding="max_length"   ,     truncation=__snake_case   ,     max_length=35   ,     ).input_ids

						hf_itm_model.load_state_dict(__snake_case		)
						hf_itm_model.eval()

						_snake_case		:	Dict							=       hf_itm_model(__snake_case   ,     __snake_case   ,     use_itm_head=__snake_case		)
						_snake_case		:	Tuple							=       hf_itm_model(__snake_case   ,     __snake_case   ,     use_itm_head=__snake_case		)

						assert out[0].item() == 0.2110687494277954
						assert torch.nn.functional.softmax(out_itm[0]   ,     dim=1		)[:, 1].item() == 0.45698845386505127

						if pytorch_dump_folder_path is not None:
												hf_itm_model.save_pretrained(pytorch_dump_folder_path + "_itm"		)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE		:       Dict      =					argparse.ArgumentParser()
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				__SCREAMING_SNAKE_CASE		:       str      =					parser.parse_args()

				convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







"""simple docstring"""
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case : Optional[Any]     		=		"""."""

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
snake_case : List[str]     		=		[
    """Assert""",
    """AssignVariableOp""",
    """EmptyTensorList""",
    """MergeV2Checkpoints""",
    """ReadVariableOp""",
    """ResourceGather""",
    """RestoreV2""",
    """SaveV2""",
    """ShardedFilename""",
    """StatefulPartitionedCall""",
    """StaticRegexFullMatch""",
    """VarHandleOp""",
]


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: Tuple ,     __snake_case: Any  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    SavedModel()
       __magic_name__      				=    []

       with open(os.path.join(__snake_case ,     'utils' ,     'tf_ops' ,     'onnx.json'  )  ) as f:
              __magic_name__      				=    json.load(__snake_case  )['opsets']

       for i in range(1 ,     opset + 1  ):
              onnx_ops.extend(onnx_opsets[str(__snake_case  )]  )

       with open(__snake_case ,     'rb'  ) as f:
              saved_model.ParseFromString(f.read()  )

       __magic_name__      				=    set()

       # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
       for meta_graph in saved_model.meta_graphs:
              # Add operations in the graph definition
              model_op_names.update(node.op for node in meta_graph.graph_def.node  )

              # Go through the functions in the graph definition
              for func in meta_graph.graph_def.library.function:
                     # Add operations in each function
                     model_op_names.update(node.op for node in func.node_def  )

    # Convert to list, sorted if you want
       __magic_name__      				=    sorted(__snake_case  )
       __magic_name__      				=    []

       for op in model_op_names:
              if op not in onnx_ops and op not in INTERNAL_OPS:
                     incompatible_ops.append(__snake_case  )

       if strict and len(__snake_case  ) > 0:
              raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops  )
       elif len(__snake_case  ) > 0:
              print(F"""Found the following incompatible ops for the opset {opset}:"""  )
              print(*__snake_case ,     sep='\n'  )
       else:
              print(F"""The saved model {saved_model_path} can properly be converted with ONNX."""  )


if __name__ == "__main__":
 snake_case : Optional[Any]     		=		argparse.ArgumentParser()
 parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
 parser.add_argument(
     """--opset""", default=1_2, type=int, help="""The ONNX opset against which the model has to be tested."""
 )
 parser.add_argument(
     """--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
 )
 parser.add_argument(
     """--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
 )
 snake_case : Any     		=		parser.parse_args()

 if args.framework == "onnx":
  onnx_compliancy(args.saved_model_path, args.strict, args.opset)